摘要
为拓展产品多目标意象设计知识获取途径,规避产品基因耦合劣势所导致的意象表达质效问题,构建了产品基因意象表达调控网络(Gene Image Expression Regulatory Network,GIERN)和应用流程。计算了产品基因公共映射关系的发生频度和倾向度,识别出基因优势耦合关系并构建产品GIERN;结合复杂网络分析和数理统计方法分析产品GIERN的结构特性,从中获取基因属性、类型和集团等作为GIERN的析出知识;评估析出知识的效能并制订其运用规则,提出析出知识与规则在产品多目标意象设计中的应用流程。以医疗救援型越野车(Off-Road Vehicle,ORV)GIERN为例,从中析出知识并将其应用于车型意象设计。结果表明:析出知识与规则能全面辅助意象设计决策并有效提升意象表达质效,从而验证产品GIERN的有效性。
In order to expand the knowledge acquisition way of product multi-objective image design,and avoid the quality and efficiency problems of image expression caused by the disadvantage of product gene coupling,a product GIERN(Gene Image Expression Regulatory Network)and its application process were constructed.The occurrence frequency and tendency of product gene common mapping relationship were calculated.The gene dominance coupling relationship was identified and used to construct product GIERN.The structural characteristics of product GIERN were analyzed combined with complex network analysis and mathematical statistics.The gene attribute,type and group were obtained as the extracted knowledge of GIERN;The efficacy of extracted knowledge was evaluated and its application rules were formulated.The application process of extracted knowledge and rules in product multi-objective image design was proposed.The medical rescue ORV(Off-Road Vehicle)GIERN was taken as an example to extract the knowledge from this GIERN and applied to the image design of this vehicle.The results showed that the extracted knowledge and rules could comprehensively assist the decision-making of image design and improve the quality and efficiency of image expression,thus verify the effectiveness of product GIERN.
作者
刘华
卫文韬
李泽珩
LIU Hua;WEI Wen-tao;LI Ze-heng(School of Design Art&Media,Nanjing University of Science&Technology,Nanjing 210094)
出处
《机械设计》
CSCD
北大核心
2022年第7期129-134,共6页
Journal of Machine Design
基金
江苏省自然科学基金青年资助项目(BK20200464)
教育部第二批新工科研究与实践项目(E-JX20201513)。
关键词
产品设计
意象设计知识
基因意象表达调控网络
复杂网络分析
感性工学
product design
image design knowledge
gene image expression regulatory network
complex network analysis
kansei engineering
